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EAGLE family: feature-conditioned and dynamically structured speculative drafts

Contribution grade

Language-model inference acceleration · Human-designed research method or resource

Reference score110,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

EAGLE conditioned feature-level drafts on one-step-advanced tokens; EAGLE2 selected context-dependent draft trees from confidence scores; EAGLE3 removed feature-regression constraints and trained multi-layer-fused token drafts using training-time rollout tests.

Scientific scope

EAGLE conditioned feature-level drafts on one-step-advanced tokens; EAGLE2 selected context-dependent draft trees from confidence scores; EAGLE3 removed feature-regression constraints and trained multi-layer-fused token drafts using training-time rollout tests. One family with three source-specific deltas, not three inventions of speculative sampling. Losslessness depends on the inherited exact verification/sampling procedure and implementation; reported speedups are model, workload, hardware and batch dependent. EAGLE2 avoids additional training beyond EAGLE, not all draft-model training; EAGLE3 changes and trains the drafter.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. One family with three source-specific deltas, not three inventions of speculative sampling. Losslessness depends on the inherited exact verification/sampling procedure and implementation; reported speedups are model, workload, hardware and batch dependent. EAGLE2 avoids additional training beyond EAGLE, not all draft-model training; EAGLE3 changes and trains the drafter.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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